scholarly journals Haptic Assistance That Restricts the Use of Redundant Solutions is Detrimental to Motor Learning

Author(s):  
Rakshith Lokesh ◽  
Rajiv Ranganathan
2019 ◽  
Author(s):  
Rakshith Lokesh ◽  
Rajiv Ranganathan

AbstractUnderstanding the use of haptic assistance to facilitate motor learning is a critical issue, especially in the context of tasks requiring control of motor variability. However, the question of how haptic assistance should be designed in tasks with redundancy, where multiple solutions are available, is currently unknown. Here we examined the effect of haptic assistance that either allowed or restricted the use of redundant solutions on the learning of a bimanual steering task. 60 college-aged participants practiced steered a single cursor placed in between their hands along a smooth W-shaped track of a certain width as quickly as possible. Haptic assistance was either applied at the ‘task’ level using a force channel that only constrained the cursor to the track, allowing for the use of different hand trajectories, or (ii) the ‘individual effector’ level using a force channel that constrained each hand to a specific trajectory. In addition, we also examined the effect of ‘fading’ – i.e., decreasing assistance with practice to reduce dependence on haptic assistance. Results showed all groups improved with practice - however, groups with haptic assistance at the individual effector level performed worse than those at the task level. Moreover, fading of assistance did not offer learning benefits over constant assistance. Overall, the results suggest that haptic assistance is not effective for motor learning when it restricts the use of redundant solutions.


2019 ◽  
Author(s):  
Jack Brookes ◽  
Faisal Mushtaq ◽  
Earle Jamieson ◽  
Aaron J. Fath ◽  
Geoffrey P. Bingham ◽  
...  

AbstractDisturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such ‘active inference’ is driven by ‘surprise’. We used these insights to create a formal model that explains why disturbance helps learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.


2019 ◽  
Vol 6 ◽  
pp. 205566831988158
Author(s):  
Simone Kager ◽  
Asif Hussain ◽  
Aamani Budhota ◽  
Wayne D Dailey ◽  
Charmayne ML Hughes ◽  
...  

Introduction Studies in robotic therapy which applied the performance enhancement approach report improvements in motor performance during training, though these improvements do not always transfer to motor learning. Objectives We postulate that there exists an assistance threshold for which performance saturates. Above this threshold, the robot’s input outweighs the patient’s input and likely learning is not fostered. This study investigated the relationship between assistance and performance changes in stroke patients to find the assistance threshold for performance saturation. Methods Twelve subacute and chronic stroke patients engaged in five sessions (over two weeks, each 60 min) in which they performed a reaching task with the rehabilitation robot H-Man in presence of varying levels of haptic assistance (50 N/m to 290 N/m, randomized order). In two additional sessions, a therapist manually tuned the assistance to promote maximal motor learning. Results Higher levels of assistance resulted in smoother and faster performance that saturated at assistance levels with K ≥  110 N/m. Also, the therapist selected assistance levels of K =  175 N/m or below. Conclusion The findings of the study indicate that low levels of assistance ( K ≤  175 N/m) can sufficiently induce a significant change in performance.


1985 ◽  
Vol 30 (3) ◽  
pp. 240-241
Author(s):  
Daniel M. Corcos
Keyword(s):  

1993 ◽  
Vol 38 (12) ◽  
pp. 1336-1336
Author(s):  
Terri Gullickson ◽  
Pamela Ramser

1954 ◽  
Author(s):  
Merrill Roff ◽  
Robert B. Payne ◽  
Edwin W. Moore

Sign in / Sign up

Export Citation Format

Share Document